Seeing and Solving Are Not Enough for Vision-Language Models

Ziheng Wang; Mingxuan Xie; Yilin Liu; Dayan Wu; Yang Li; Pengwen Dai

cs.CVSep 27, 2026

Seeing and Solving Are Not Enough for Vision-Language Models

Authors: Ziheng Wang, Mingxuan Xie, Yilin Liu, Dayan Wu, Yang Li, Pengwen Dai

Organizations: Sun Yat-sen University · Zhejiang University · The Hong Kong University of Science and Technology · Institute of Information Engineering · Hunan University

Abstract

Vision-language models (VLMs) answer visual questions by combining visual information extraction with downstream problem solving. We investigate a fundamental question: Does an incorrect answer necessarily reflect a failure in visual extraction or problem solving? A model may succeed at both abilities when tested separately yet still fail on the original multimodal question, a distinction that overall answer accuracy cannot reveal. To study this, we perform a question-level empirical analysis across multiple VLMs and visual domains. We define an exactly scorable task state (i.e., the visual information sufficient to solve a question) and use it to test whether the same model can extract the required state, solve the question from the ground-truth state, and answer the original multimodal question. We find that composition failures, where extraction and solving both succeed but direct answering fails, account for 17.7% to 75.6% of direct-answering errors across multiple VLMs and datasets. To address this failure mode, we introduce a simple yet effective method, termed State Realization Tuning (SRT). SRT fine-tunes LoRA adapters attached to the language-model layers while keeping the pretrained VLM weights frozen. It trains the model to output the ground-truth task state before the final answer in a single autoregressive response. SRT improves over standard supervised fine-tuning by 1.7 to 14.1 percentage points and repairs 92.5% to 98.1% of diagnosed composition failures. A single LoRA adapter trained with SRT also improves performance across substantially different task-state structures. Our work shows that having both visual extraction and problem-solving capabilities does not guarantee correct multimodal answering. Requiring the model to first output the visual information needed to solve the question can help bridge this gap.

Figures & tables

Figure 1: An example of a composition failure. For the same question, the model succeeds at both Extract and Solve, yet fails to answer directly. Panel (a) shows the task-state components schematically. In the actual evaluation, Extract and Direct each receive the same full task question in a single call, without the illustrative scene-content lists (Appendix G ).

Figure 2: State Realization Tuning with LoRA. Only the SRT adapter, implemented as LoRA updates in the language-model layers, is trained. The task state and final answer are two fields of one autoregressive response, with the state generated first.

		Accuracy			Failures		Error breakdown
Dataset	Model	Extract	Solve	Direct	CFS	CFR
ChartQA	Qwen	81.1	86.8	63.5	40.2	20.5
	Molmo	75.5	94.3	58.0	65.5	38.2
	InternVL	90.3	97.6	66.6	75.6	28.7
	MiniCPM	77.2	98.1	61.6	56.5	28.8
CLEVR	Qwen	56.3	94.5	37.5	46.1	54.2

Table 1: Accuracy and composition-failure prevalence. CFS is the share of Direct errors that are composition failures. CFR is the Direct failure rate on questions where both Extract and Solve succeed. The bars partition each model’s Direct errors. All values are percentages.

			Variants
Dataset	Model	Untuned	Standard SFT	Format Control	Answer-to-State	SRT
ChartQA	Qwen	63.5	$85.1\pm 0.8$	$85.8\pm 0.7$	$85.1\pm 0.6$	$\mathbf{91.7\pm 0.4}$
	Molmo	58.0	$77.3\pm 0.6$	$75.7\pm 2.4$	$79.4\pm 1.0$	$\mathbf{91.5\pm 0.6}$
	InternVL	66.6	$87.7\pm 0.8$	$86.2\pm 0.8$	$87.4\pm 1.2$	$\mathbf{94.0\pm 0.4}$
	MiniCPM	61.6	$72.8\pm 1.3$	$72.3\pm 1.7$	$73.0\pm 1.0$	$\mathbf{86.0\pm 0.4}$
CLEVR	Qwen	37.5	93.7	92.9	93.5	$\mathbf{95.4}$

Table 2: Answer accuracy under various conditions. Untuned is the model before fine-tuning. ChartQA results are the mean

\pm

standard deviation over three seeds; CLEVR results use one seed. All values are percentages.

Figure 3: A qualitative example of SRT on CLEVR. Standard SFT produces an incorrect answer, whereas SRT first realizes the correct task state and then derives the correct answer.

		State realization		CF Repair		Preservation
Dataset	Model	State Acc.	S–A Cons.	Standard SFT	SRT	Preservation
ChartQA	Qwen	$89.1\pm 0.2$	$99.2\pm 0.5$	$73.0\pm 4.5$	$\mathbf{94.3\pm 1.1}$	$98.8\pm 0.1$
	Molmo	$88.1\pm 0.8$	$99.3\pm 0.1$	$71.4\pm 0.2$	$\mathbf{98.1\pm 1.4}$	$98.5\pm 0.6$
	InternVL	$91.7\pm 0.6$	$99.8\pm 0.2$	$82.4\pm 1.7$	$\mathbf{97.5\pm 0.4}$	$99.2\pm 0.3$
	MiniCPM	$81.2\pm 1.0$	$99.8\pm 0.1$	$58.3\pm 3.0$	$\mathbf{92.5\pm 2.4}$	$97.1\pm 0.1$
CLEVR	Qwen	93.9	100.0	95.3	$\mathbf{96.8}$	97.0

Table 3: State realization, failure repair, and preservation. State metrics and Preservation refer to SRT. CF Repair compares Standard SFT and SRT on fixed pre-tuning composition-failure sets. Values are percentages, with three-seed standard deviations on ChartQA.

Model	Untuned	Standard SFT	Extract $\rightarrow$ Solve	SRT
Qwen	63.5	$85.1\pm 0.8$	75.1	$\mathbf{91.7\pm 0.4}$
Molmo	58.0	$77.3\pm 0.6$	79.7	$\mathbf{91.5\pm 0.6}$
InternVL	66.6	$87.7\pm 0.8$	90.7	$\mathbf{94.0\pm 0.4}$
MiniCPM	61.6	$72.8\pm 1.3$	82.1	$\mathbf{86.0\pm 0.4}$

Table 4: Inference-time composition versus supervised fine-tuning on ChartQA. Extract

\rightarrow

Solve passes the predicted state to a separate image-free Solve call. Fine-tuning results are three-seed mean

\pm

standard deviation. All values are percentages.

Task structure	Qwen	Molmo
Multi-step arithmetic	96.2 (25/26)	86.0 (49/57)
Conditional lookup	50.8 (33/65)	50.0 (2/4)
Relational count ranking	80.0 (28/35)	N/A (0/0)
Color-set operations	45.0 (68/151)	56.8 (25/44)
Overall CFR	55.6 (154/277)	72.4 (76/105)

Table 5: Composition failures with richer task states. Untuned CFR (%) is shown with CF

/J

counts. N/A means no question passes both Extract and Solve (

J=0

		Qwen		Molmo
Task structure	$N$	Standard SFT	SRT	Standard SFT	SRT
Multi-step arithmetic	291	32.3	67.7	22.7	68.7
Conditional lookup	215	71.6	85.1	67.4	84.2
Relational count ranking	400	87.8	96.5	38.5	80.0
Color-set operations	400	79.5	98.5	73.5	91.0
Average accuracy	1,306	70.2	88.8	50.5	81.5

Table 6: SRT on additional task structures. Final-answer accuracy (%) of the Standard SFT and SRT adapters on each structure’s

N

test questions.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Setting	Model	$N$	$J$	CF	CFS	CFR
ChartQA	Qwen	1,077	770	158	40.2	20.5
ChartQA	Molmo	1,077	774	296	65.5	38.2
ChartQA	InternVL	1,077	948	272	75.6	28.7
ChartQA	MiniCPM	1,077	813	234	56.5	28.8
CLEVR	Qwen	33,723	17,916	9,708	46.1	54.2
CLEVR	Molmo	33,723	7,014	4,044	17.7	57.7

Appendix

Table 7: Exact counts underlying the composition-failure diagnosis.

J

is the number of questions on which both Extract and Solve succeed; CF is the subset for which Direct fails.

Setting	Model	Direct errors	$E{=}0,S{=}0$	$E{=}0,S{=}1$	$E{=}1,S{=}0$	$E{=}1,S{=}1$
ChartQA	Qwen	393	38 (9.7)	106 (27.0)	91 (23.2)	158 (40.2)
ChartQA	Molmo	452	13 (2.9)	133 (29.4)	10 (2.2)	296 (65.5)
ChartQA	InternVL	360	2 (0.6)	74 (20.6)	12 (3.3)	272 (75.6)
ChartQA	MiniCPM	414	3 (0.7)	165 (39.9)	12 (2.9)	234 (56.5)
CLEVR	Qwen	21,076	783 (3.7)	9,550 (45.3)	1,035 (4.9)	9,708 (46.1)

Appendix

Table 8: Direct errors broken down by Extract and Solve outcomes. Counts, with the percentage of Direct errors in parentheses. The last column is the composition-failure set

\mathcal{C}

; the first two together form

\mathcal{E}

and the third is

\mathcal{S}

in Equation 4 .

Model	Operation	CF count	CFS	CFR
Qwen	Comparison	53	29.0	26.4
	Difference	55	52.4	19.9
	Sum	50	47.6	17.1
Molmo	Comparison	60	75.0	24.8
	Difference	107	59.8	43.1
	Sum	129	66.8	45.4

Appendix

Table 9: Composition failures by operation on ChartQA. CFS and CFR are percentages.

Setting	Model	Direct (strict)	Direct (content)	CFS (content)	CFR (content)
ChartQA	Molmo	58.0	61.6	63.5	34.0
ChartQA	MiniCPM	61.6	61.6	56.5	28.8
CLEVR	Qwen	37.5	38.6	45.5	52.6

Appendix

Table 10: Sensitivity to content-normalized Direct scoring. Extract and Solve outcomes are held fixed; only final-answer presentation is normalized.

Model	Canonical CFR	Paraphrase A	Paraphrase B
Molmo	34.0	34.5	30.2
MiniCPM	28.8	28.8	27.6

Appendix

Table 11: Composition failures persist across prompt paraphrases. Values are CFR (%); all three wording sets use content-normalized scoring.

Setting	Model	Reversed	$J$	CF	CFR
ChartQA	Qwen	10	770 $\rightarrow$ 780	158 $\rightarrow$ 158	20.5 $\rightarrow$ 20.3
ChartQA	Molmo	31	774 $\rightarrow$ 805	296 $\rightarrow$ 303	38.2 $\rightarrow$ 37.6
ChartQA	InternVL	3	948 $\rightarrow$ 951	272 $\rightarrow$ 272	28.7 $\rightarrow$ 28.6
ChartQA	MiniCPM	10	813 $\rightarrow$ 823	234 $\rightarrow$ 236	28.8 $\rightarrow$ 28.7
CLEVR	Qwen	212	17,916 $\rightarrow$ 18,128	9,708 $\rightarrow$ 9,793	54.2 $\rightarrow$ 54.0

Appendix

Table 12: Sensitivity of the diagnosis to accepting reversed pairs on sum and absolute-difference questions. “Reversed” counts Extract outputs that fail strict scoring only because the two values are swapped. Strict

\rightarrow

relaxed values are shown for

J

, CF, and CFR (%).

Model	Solve (text-only)	Image + ground-truth task state	Image + counterfactual state
Qwen	86.8	85.4	45.7
Molmo	94.3	92.1	38.4

Appendix

Table 13: Solve accuracy on ChartQA with and without the image. A task state is supplied in text in each condition. The counterfactual condition replaces the ground-truth state with an answer-changing incorrect state. Invalid-output rates for Molmo are

3.0\%

(image + ground-truth) and

7.9\%

(image + counterfactual). Values are percentages.

Setting	Model	Standard SFT	Format Control	Answer-to-State	SRT	$\Delta$ Format	$\Delta$ Ans.-to-State
ChartQA	Qwen	85.1	85.8	85.1	91.7	+5.9	+6.7
ChartQA	Molmo	77.3	75.7	79.7	91.6	+15.9	+11.9
ChartQA	InternVL	87.7	86.2	87.4	94.0	+7.8	+6.6
ChartQA	MiniCPM	72.8	72.3	73.0	86.0	+13.7	+13.0
CLEVR	Qwen	93.7	92.9	93.5	95.4	+2.5	+1.9

Appendix

Table 14: Final-answer accuracy under content-only scoring. The auxiliary field is ignored. ChartQA values are three-seed means. The last two columns show SRT’s gains over the two controls in percentage points, computed from unrounded means.

Setting	Model	Seed	Wrong state, answer correct	Correct state, answer wrong
ChartQA	Qwen	13	36 / 120	10 / 957
		42	36 / 115	7 / 962
		87	33 / 117	3 / 960
	Molmo	13	38 / 125	1 / 951
		42	37 / 122	3 / 955
		87	40 / 132	1 / 939

Appendix

Table 15: Generated states and final answers under SRT. Each fraction counts the indicated answer outcome among questions with the indicated state outcome. ChartQA Molmo outputs with an undefined state (1, 0, and 6 per seed) are excluded. Additional task-structure results use the Qwen adapter from Table 6 .

Figure 4: State correctness and state–answer consistency are distinct. (a) On ChartQA, Qwen gives an incorrect answer that is consistent with its incorrect generated state. (b) Molmo extracts the three values correctly but computes the wrong answer. The latter example comes from the additional multi-step arithmetic experiment. Questions and outputs are verbatim.

Model	Direct	Zero-shot State-first	Invalid	SRT
Qwen	63.5	59.1	0.1	91.7
Molmo	58.0	53.4	5.7	91.5
InternVL	66.6	11.6	78.8	94.0
MiniCPM	61.6	53.9	13.6	86.0

Appendix

Table 16: Zero-shot state-first prompting on ChartQA. Direct and zero-shot answers use the same canonical final-answer parser. Invalid zero-shot outputs count as incorrect. SRT gives the corresponding three-seed fine-tuning mean. All values are percentages.

Model	Family	$N$	$J$	CF	CFS	CFR	95% CI
Qwen	Multi-step	291	26	25	9.2	96.2	[80.4, 99.9]
	Conditional lookup	215	65	33	26.0	50.8	[38.1, 63.4]
	Relational count ranking	400	35	28	10.9	80.0	[63.1, 91.6]
	Color-set	400	151	68	31.5	45.0	[36.9, 53.3]
Molmo	Multi-step	291	57	49	17.4	86.0	[74.2, 93.7]
	Conditional lookup	215	4	2	1.1	50.0	[6.8, 93.2]

Appendix

Table 17: Untuned diagnosis on the additional task structures.

J

counts questions where Extract and Solve both succeed; CF counts composition failures. The last column gives the exact two-sided

95\%

Clopper–Pearson interval for CFR. N/A means that CFR and its interval cannot be computed because

J=0

. These intervals describe uncertainty over the evaluated questions, not variation across training seeds.

Model	Family	State Acc.	S–A Cons.
Qwen	Multi-step	84.2	81.8
	Conditional lookup	79.5	96.2
	Relational count ranking	78.5	100.0
	Color-set	97.5	99.3
Molmo	Multi-step	80.8	83.7
	Conditional lookup	78.6	99.1

Appendix

Table 18: State realization on the additional task structures. Results use the multi-task SRT adapters from Table 6 . State Acc. is exact task-state accuracy over all questions. S–A Cons. measures whether the answer follows from the generated state, among outputs where the state defines an answer and the final answer can be parsed. Values are percentages.

Figure 5: A qualitative example of SRT on ChartQA. For Qwen, Standard SFT produces an incorrect answer, whereas SRT first realizes the correct task state and then computes the sum correctly.

Figure 6: Relational count ranking and color-set difference. SRT lists the three relation-conditioned counts before choosing the middle group, and both color sets before taking their difference.

Figure 7: Three-value arithmetic and conditional lookup. SRT states three numerical values or a lookup table before answering.

Figure 8: State realization across chart appearances and operations. Qwen compares two values in a line chart and computes an absolute difference in a stacked-bar chart. Molmo states three values before a multi-step calculation on a different line chart. Panel (c) belongs to the additional task structures, not the pair-state ChartQA comparison. All outputs are verbatim.

Figure 9: Composition within the CLEVR count queries. The first query combines material and a union of object groups, while the second uses a spatial relation. SRT gives both counts and their correct sum, whereas Standard SFT answers incorrectly. Question and outputs are verbatim from Qwen.

Condition	Image	Full question	State supplied	Output
Extract	Yes	Yes	No	Task state
Solve	No	Yes	Yes	Final answer
Direct	Yes	Yes	No	Final answer
Standard SFT	Yes	Yes	No	Final answer
Format Control	Yes	Yes	No	Fixed placeholder, then answer
Answer-to-State	Yes	Yes	No	Answer, then task state

Appendix

Table 19: Information provided to each condition. The state column indicates whether the ground-truth task state is supplied as input. For fine-tuning, the output column gives the assistant supervision target; at test time the model generates that output itself. All conditions receive the full task question.

Method	Assistant target
Standard SFT	FINAL_ANSWER: $y$
Format Control	CHECK_STATE: X FINAL_ANSWER: $y$
Answer-to-State	FINAL_ANSWER: $y$ TASK_STATE: $a$ , $b$
SRT	TASK_STATE: $a$ , $b$ FINAL_ANSWER: $y$

Appendix

Table 20: Training targets for the pair-state tasks. Here

a,b

are the ground-truth values or counts,

y

is the answer, and X is the fixed, task-independent placeholder. Field names are literal, including the underscore. Each displayed line is one output line.

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Vision-language models (VLMs) can answer simple visual questions, but often struggle when one question requires several visual judgments. We study this gap with controlled tasks for feature binding, numerosity, spatial relations, and amodal completion, together with a Composite task that combines them. Matched counterfactual image pairs isolate changes in the visual evidence needed to answer. Across four models, direct answers, hidden-state readouts, and state interventions show that the individual judgments can be made without explicit reasoning and that intervening on the corresponding states can affect the answer. During reasoning, the Composite answer becomes decodable from hidden states and usable from shortened traces, often before the model stops on its own. We train a small detector to predict this readiness and stop reasoning at that point. On MMStar and RealWorldQA, this reduces mean reasoning tokens by 79.1% and 74.5%, while average accuracy rises by 3.13 and 3.30 percentage points, respectively. These findings connect the internal development of answer readiness to a practical rule for allocating reasoning computation.

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